from typing import Any, Optional

from .solver_wrapper_base import SolverWrapperBase, ProblemDefinitionBase, FENICSX_AVAILABLE
from .simulation_parameters import NonlinearSolverSettings, SolverSettings # 确保导入 NonlinearSolverSettings

if FENICSX_AVAILABLE:
    from mpi4py import MPI
    from dolfinx import fem
    from dolfinx.fem.petsc import NonlinearProblem # 用于 NewtonSolver 的参数
    from dolfinx.nls.petsc import NewtonSolver # NewtonSolver 的正确导入路径
    from petsc4py import PETSc # SNES 对象 (如果直接使用 PETSc SNES)
else:
    # 虚拟类型
    class MPI:
        class Comm: pass
    class fem:
        class NewtonSolver: pass
        class Function: pass # 模拟 dolfinx.fem.Function
    class NonlinearProblem: # 模拟 dolfinx.fem.petsc.NonlinearProblem
        def __init__(self, F, u, bcs=[], J=None): pass
    class PETSc:
        class SNES: pass
    # class NonlinearSolverSettings: pass # 已从 simulation_parameters 导入

class NonlinearSolverWrapper(SolverWrapperBase):
    """
    封装 dolfinx.fem.NewtonSolver (或 PETSc SNES) 的非线性求解器包装器。
    """

    def __init__(self,
                 problem_definition: ProblemDefinitionBase,
                 solver_params: SolverSettings, # 使用通用的 SolverSettings
                 comm: Optional[MPI.Comm]):
        """
        初始化 NonlinearSolverWrapper。

        参数:
            problem_definition (ProblemDefinitionBase):
                此求解器将要解决的物理问题的定义实例。
            solver_params (SolverSettings): # 改为 SolverSettings
                包含求解器配置的参数对象。非线性特定设置应在其 .nonlinear_solver_config 属性中。
            comm (Optional[MPI.Comm]):
                MPI 通信器。
        """
        super().__init__(problem_definition, solver_params, comm)
        
        self._is_initialized_correctly = False # 标记求解器是否成功初始化
        self.nonlinear_solver_settings: Optional[NonlinearSolverSettings] = self.solver_params.nonlinear_solver_config

        if self.nonlinear_solver_settings is None:
            print(f"警告: {self.problem_definition.problem_name} 的非线性求解器配置缺失。将使用默认或模拟行为。")
            # 如果需要，可以在这里创建一个默认的 NonlinearSolverSettings
            # self.nonlinear_solver_settings = NonlinearSolverSettings()


        if not FENICSX_AVAILABLE:
            print("FEniCSx 不可用，将使用模拟的非线性求解器对象进行 NonlinearSolverWrapper。")
            class MockNonlinearSolver:
                def __init__(self, comm_inner, problem_inner=None, u_inner=None): # 调整以匹配 NewtonSolver 构造
                    self.comm = comm_inner
                    self.iterations = 0
                    self.converged = False
                    self.n = 0 # 模拟 NewtonSolver().solve() 返回值
                    self.is_converged_flag = False # 模拟 NewtonSolver().solve() 返回值
                
                def solve(self, u_vector_ignored): # NewtonSolver.solve 接受 u.vector
                    self.n += 1
                    self.is_converged_flag = True 
                    # 模拟属性设置
                    self.iterations = self.n # NewtonSolver 没有 .iterations 属性，它通过 solve 返回
                    self.converged = self.is_converged_flag
                    return (self.n, self.is_converged_flag)

                @property # 模拟 NewtonSolver 的属性
                def R_norm(self): return 0.0 
                @property
                def dR_norm(self): return 0.0

            # 模拟 NewtonSolver 的创建
            self._solver_object = MockNonlinearSolver(self.comm)
            self._is_initialized_correctly = True
            return

        # FEniCSx 可用时的逻辑
        if self._comm is None:
            print(f"警告: MPI 通信器未提供给 NonlinearSolverWrapper '{self.problem_definition.problem_name}'，求解器对象未创建。")
            return

        # 获取必要的 UFL forms 和求解变量
        F_form = self.problem_definition.get_residual_form()
        J_form = self.problem_definition.get_jacobian_form() # J 是可选的
        u_solve = self.problem_definition.get_variable_to_solve()
        bcs = self.problem_definition.get_boundary_conditions()

        if F_form is None:
            print(f"错误: 残差形式 F (R) 未在问题定义 '{self.problem_definition.problem_name}' 中提供，无法创建非线性求解器。")
            return
        if u_solve is None:
            print(f"错误: 求解变量 u 未在问题定义 '{self.problem_definition.problem_name}' 中提供，无法创建非线性求解器。")
            return

        try:
            # 决定使用 dolfinx.fem.NewtonSolver还是 PETSc SNES
            solver_choice = "newton" # 默认为 NewtonSolver
            if self.nonlinear_solver_settings and self.nonlinear_solver_settings.solver_type:
                solver_choice = self.nonlinear_solver_settings.solver_type
            
            if solver_choice == "newton":
                # 在创建 NewtonSolver 之前，尝试通过 PETSc.Options() 设置 KSP/PC 类型
                if FENICSX_AVAILABLE and self.nonlinear_solver_settings and self.nonlinear_solver_settings.custom_options:
                    try:
                        from petsc4py import PETSc
                        opts = PETSc.Options()
                        custom_opts_dict = self.nonlinear_solver_settings.custom_options
                        
                        print(f"[NWrapper __init__] 尝试通过 PETSc.Options() 设置: {custom_opts_dict}")
                        
                        if "ksp_type" in custom_opts_dict and custom_opts_dict["ksp_type"] is not None:
                            opts["ksp_type"] = custom_opts_dict["ksp_type"]
                            print(f"  PETSc.Options() ksp_type 设置为: {custom_opts_dict['ksp_type']}")
                        if "pc_type" in custom_opts_dict and custom_opts_dict["pc_type"] is not None:
                            opts["pc_type"] = custom_opts_dict["pc_type"]
                            print(f"  PETSc.Options() pc_type 设置为: {custom_opts_dict['pc_type']}")
                        # 其他需要的全局 SNES/KSP 选项也可以在这里添加
                        # 例如: opts["snes_linesearch_type"] = "basic"
                        #       opts["snes_max_it"] = self.nonlinear_solver_settings.max_iterations
                        #       opts["snes_atol"] = self.nonlinear_solver_settings.absolute_tolerance
                        #       opts["snes_rtol"] = self.nonlinear_solver_settings.relative_tolerance
                        
                    except ImportError:
                        print("警告: 无法导入 petsc4py，无法通过 PETSc.Options() 设置 KSP/PC 类型。")
                    except Exception as e_petsc_opts:
                        print(f"警告: 通过 PETSc.Options() 设置 KSP/PC 类型时出错: {e_petsc_opts}")

                self.fem_nonlinear_problem = NonlinearProblem(F_form, u_solve, bcs=bcs, J=J_form)
                print(f"[NWrapper __init__] 创建 NewtonSolver")
                self._solver_object = NewtonSolver(self._comm, self.fem_nonlinear_problem)
                
                # NewtonSolver 的公差和最大迭代次数等参数通过其自身的 set_parameters 方法设置
                # （如果 NewtonSolver 类本身没有这些参数，那么它们也需要通过 PETSc.Options() 设置，并冠以SNES前缀）
                # 根据 DOLFINx NewtonSolver 的 API，它似乎不直接接受这些，所以全局 PETSc 选项是主要途径

                # 应用来自 nonlinear_solver_settings 的参数
                if self.nonlinear_solver_settings:
                    self._solver_object.atol = self.nonlinear_solver_settings.absolute_tolerance
                    self._solver_object.rtol = self.nonlinear_solver_settings.relative_tolerance
                    self._solver_object.max_it = self.nonlinear_solver_settings.max_iterations
                    self._solver_object.relaxation_param = self.nonlinear_solver_settings.relaxation_parameter
                    # NewtonSolver.report 属性似乎不存在了，行为由日志级别控制
                    # if self.nonlinear_solver_settings.report:
                    #     # dolfinx.log.set_log_level(dolfinx.log.LogLevel.INFO) # 或其他
                    #     pass 
                    self._solver_object.error_on_nonconvergence = self.nonlinear_solver_settings.error_on_nonconvergence
                    
                    # 如果有自定义 PETSc 选项，传递给 KSP (NewtonSolver 内部的线性求解器)
                    if self.nonlinear_solver_settings.custom_options:
                        for k, v in self.nonlinear_solver_settings.custom_options.items():
                            # NewtonSolver 有一个 .ksp 属性可以访问内部的 KSP 对象
                            # self._solver_object.ksp.setOptionsPrefix(f"problem_{self.problem_definition.problem_name}_nl_")
                            # option_key = f"{self._solver_object.ksp.getOptionsPrefix()}{k}"
                            # PETSc.Options().setValue(option_key, str(v))
                            # 更简单的方式可能是直接设置
                            # 例如: self._solver_object.ksp.setType("gmres")
                            # 但 custom_options 通常是字符串键值对，需要更复杂的解析
                            # 或者期望用户提供 -snes_ksp_ ... 形式的选项
                            pass # 暂时跳过复杂的 PETSc 选项直接传递
                
                print(f"NonlinearSolverWrapper 为问题 '{self.problem_definition.problem_name}' 初始化了 dolfinx.fem.NewtonSolver。")
                self._is_initialized_correctly = True

            elif solver_choice == "snes":
                # PETSc SNES 的初始化 (更复杂，这里保持骨架)
                self._solver_object = PETSc.SNES().create(self._comm)
                # ... 此处进行 SNES 的详细设置 ...
                # 例如:
                # self._solver_object.setType(self.nonlinear_solver_settings.snes_type if self.nonlinear_solver_settings else "newtonls")
                # self._solver_object.setTolerances(rtol=self.nonlinear_solver_settings.relative_tolerance, 
                #                              max_it=self.nonlinear_solver_settings.max_iterations,
                #                              atol=self.nonlinear_solver_settings.absolute_tolerance)
                # 定义并设置 SNES 的 function 和 jacobian
                # 这通常涉及创建 PETSc Vec 和 Mat，以及使用 dolfinx.fem.petsc.assemble_vector/assemble_matrix
                print(f"NonlinearSolverWrapper 为问题 '{self.problem_definition.problem_name}' 初始化了 PETSc SNES (骨架)。")
                print("  注意: 完整的 PETSc SNES 实现需要详细的回调函数和矩阵/向量设置。")
                self._is_initialized_correctly = True # 假设骨架初始化成功

            elif solver_choice == "petsc_snes_experimental":
                # 这个分支目前是高度实验性的，并且可能不完整
                print("信息: 正在尝试初始化 PETSc SNES 求解器 (实验性)...")
                petsc_imported_successfully = False # 用于跟踪 PETSc 是否成功导入
                if FENICSX_AVAILABLE:
                    try:
                        from petsc4py import PETSc
                        petsc_imported_successfully = True
                        # 真正的 SNES 初始化会更复杂，需要设置回调函数等。
                        # snes = PETSc.SNES().create(self._comm)
                        # self._solver_object = snes # 示例
                        print("信息: PETSc SNES 骨架已创建 (模拟)。实际使用需要更多设置。")
                        self._is_initialized_correctly = True # 假设骨架创建成功
                    except ImportError:
                        print("警告: 无法导入 petsc4py，PETSc SNES 求解器不可用。")
                        self._is_initialized_correctly = False
                else: # FENICSX_AVAILABLE is False
                    print("信息: FEniCSx 环境不可用，跳过 PETSc SNES 求解器的实际初始化。")
                    self._is_initialized_correctly = False # 或者 True，取决于作为"骨架"的定义

                if not self._is_initialized_correctly:
                    # 安全地记录 PETSc 导入状态
                    print(f"错误: PETSc SNES 求解器 '{self.problem_definition.problem_name}' 初始化失败 (FENICSX_AVAILABLE={FENICSX_AVAILABLE}, petsc4py 模块是否已加载: {petsc_imported_successfully})")

            else:
                print(f"错误: 未知的非线性求解器类型 '{solver_choice}'。")
        
        except AttributeError as e:
            print(f"错误: 初始化非线性求解器时发生 AttributeError: {e}。可能是 FEniCSx 版本不兼容或 PETSc 配置问题。")
        except Exception as e:
            print(f"错误: 初始化非线性求解器时发生未知错误: {e}")

        if not self._is_initialized_correctly:
            # 修正日志消息，使其不依赖于 PETSc 是否已成功导入
            petsc_imported_status = "是" if 'PETSc' in locals() or 'PETSc' in globals() else "否"
            print(f"错误: PETSc SNES 求解器 '{self.problem_definition.problem_name}' 初始化失败 (FENICSX_AVAILABLE={FENICSX_AVAILABLE}, PETSc 是否已导入: {petsc_imported_status})")
            # 进一步的骨架设置...


    def solve(self) -> bool:
        """
        使用配置的非线性求解器 (dolfinx.fem.NewtonSolver 或 PETSc SNES) 求解非线性问题。

        返回:
            bool: 如果求解过程收敛，则返回 True，否则返回 False。
        """
        if not self._is_initialized_correctly or self._solver_object is None:
            print(f"错误: 非线性求解器 '{self.problem_definition.problem_name}' 未正确初始化或不可用，无法执行 solve()。")
            self._is_converged = False
            self._num_iterations = 0
            return False

        u_to_solve = self.problem_definition.get_variable_to_solve()
        if not FENICSX_AVAILABLE and isinstance(self._solver_object, fem.NewtonSolver): # 模拟求解器情况
             # NewtonSolver 的模拟版本
            try:
                print(f"模拟 NonlinearSolverWrapper.solve() (NewtonSolver) 调用 (FEniCSx 不可用), 问题: {self.problem_definition.problem_name}")
                num_iter_solver, converged_flag_solver = self._solver_object.solve(u_to_solve.x.array()) # 模拟 solve 可能需要数组
                self._num_iterations = num_iter_solver
                self._is_converged = converged_flag_solver
            except Exception as e:
                print(f"在模拟的 NonlinearSolverWrapper.solve() (NewtonSolver) 期间发生错误: {e}")
                self._is_converged = False
            return self._is_converged
        
        if isinstance(self._solver_object, NewtonSolver): # 处理真实的 NewtonSolver (使用导入的 NewtonSolver)
            print(f"NonlinearSolverWrapper.solve() (NewtonSolver) 调用问题 '{self.problem_definition.problem_name}'")
            try:
                print(f"调用 dolfinx.nls.petsc.NewtonSolver.solve() 处理问题 '{self.problem_definition.problem_name}' 的变量 '{u_to_solve.name if hasattr(u_to_solve, 'name') else '未知变量'}'...")
                # NewtonSolver.solve(u_function) 返回 (迭代次数, 是否收敛布尔值)
                num_its, converged_bool = self._solver_object.solve(u_to_solve) 
                
                self._num_iterations = num_its
                self._is_converged = converged_bool
                
                print(f"  NewtonSolver.solve() 返回: 迭代次数 = {self._num_iterations}, 是否收敛 = {self._is_converged}")

                if self._is_converged:
                    print(f"  NewtonSolver 对 '{self.problem_definition.problem_name}' 收敛于 {self._num_iterations} 次迭代。")
                else:
                    if self._num_iterations == 0:
                        print(f"  NewtonSolver 对 '{self.problem_definition.problem_name}' 在 {self._num_iterations} 次迭代后未收敛 (可能内部线性求解失败或问题已满足容差但未标记为收敛)。")
                    else:
                        print(f"  NewtonSolver 对 '{self.problem_definition.problem_name}' 在 {self._num_iterations} 次迭代后未收敛。")
            except Exception as e_solve:
                print(f"在 NonlinearSolverWrapper.solve() (NewtonSolver) 期间发生未知错误: {e_solve}")
                self._num_iterations = 0
                self._is_converged = False
            
            # 返回 self._is_converged，以便测试可以断言
            return self._is_converged

        elif isinstance(self._solver_object, PETSc.SNES): # 处理真实的 PETSc SNES
            # 此处应包含调用 PETSc SNES.solve() 的逻辑
            print(f"调用 PETSc.SNES.solve() 处理问题 '{self.problem_definition.problem_name}' (骨架实现)...")
            print("  注意: 实际的 PETSc SNES 求解逻辑需要在此实现。")
            try:
                # 假设 u_to_solve.x 是一个 PETSc Vec (Function.x 通常是)
                # self._solver_object.solve(None, u_to_solve.x) # PETSc SNES.solve(b, x) - 对于非线性问题 b 通常是 None
                self._num_iterations = 1 # 模拟迭代
                self._is_converged = True # 模拟收敛
                # self._num_iterations = self._solver_object.getIterationNumber()
                # self._is_converged = self._solver_object.getConvergedReason() > 0

                if self._is_converged:
                    print(f"  PETSc SNES 报告已收敛。迭代次数: {self._num_iterations} (模拟)。")
                else:
                    print(f"  PETSc SNES 未能收敛。迭代次数: {self._num_iterations} (模拟)。")
                return self._is_converged
            except Exception as e:
                print(f"在 NonlinearSolverWrapper.solve() (PETSc SNES) 期间发生错误: {e}")
                self._is_converged = False
                return False
        else:
            print(f"错误: 未知的求解器对象类型 '{type(self._solver_object)}'，无法执行 solve()。")
            self._is_converged = False
            return False

    def update_problem_forms(self) -> None:
        """
        如果底层的非线性问题 (fem.petsc.NonlinearProblem) 或 PETSc SNES 的回调函数
        依赖于可能已更改的外部数据 (例如 ProblemDefinition 中的形式)，则可能需要更新。
        对于 dolfinx.fem.NewtonSolver，它在创建时会链接到 NonlinearProblem 实例。
        如果 NonlinearProblem 内部的 F 或 J 表单依赖于会改变的 fem.Constant 或 fem.Function，
        它们将在 assemble 时自动获取最新值。
        因此，对于 NewtonSolver，此方法可能不需要做太多事情，除非 NonlinearProblem 本身需要重建。
        """
        super().update_problem_forms()
        if FENICSX_AVAILABLE and self._is_initialized_correctly:
            print(f"NonlinearSolverWrapper.update_problem_forms() 被调用为 '{self.problem_definition.problem_name}'。")
            
            current_solver_type = "unknown"
            if self.nonlinear_solver_settings and self.nonlinear_solver_settings.solver_type:
                current_solver_type = self.nonlinear_solver_settings.solver_type

            if current_solver_type == "newton" and isinstance(self._solver_object, NewtonSolver):
                print("  检测到求解器是 NewtonSolver。通常不需要特殊操作，因为形式会在组装时更新。")
                # 如果将来需要对 fem_nonlinear_problem 进行操作，可以在这里添加:
                # if hasattr(self, 'fem_nonlinear_problem') and self.fem_nonlinear_problem is not None:
                #     print("    fem_nonlinear_problem 存在。")
                # else:
                #     print("    警告: solver_type 是 newton 但 fem_nonlinear_problem 未设置。")
            elif current_solver_type == "snes" and hasattr(self._solver_object, 'setFunction') and hasattr(self._solver_object, 'setJacobian'): # 模拟 PETSc SNES 检查
                print("  检测到求解器是 PETSc SNES。可能需要重新设置函数/雅可比矩阵的回调或相关的 PETSc 对象。")
            else:
                print(f"  求解器类型 '{current_solver_type}' 或对象类型 '{type(self._solver_object).__name__}' 不需要或不支持在 update_problem_forms 中进行特殊处理。")
            pass


if __name__ == '__main__':
    # 此 __main__ 部分仅用于基本演示，可能无法直接运行，因为它依赖于
    # 许多模拟的或未完全定义的组件 (如 ProblemDefinitionBase 的具体子类等)。
    print("NonlinearSolverWrapper 子类已定义。")

    # 模拟必要的参数和环境
    class MockSimParams:
        class NonlinearConfig:
            solver_type = "newton"
            absolute_tolerance = 1e-9
            relative_tolerance = 1e-7
            max_iterations = 30
            relaxation_parameter = 1.0
            report = True
            error_on_nonconvergence = False
            custom_options = None
        nonlinear_solver_config = NonlinearConfig()

    class MockProblemDef(ProblemDefinitionBase):
        def __init__(self, name, comm_inner):
            self.problem_name = name
            self.comm = comm_inner
            # 模拟 FEniCSx 对象 (如果可用)
            if FENICSX_AVAILABLE:
                # 需要一个简单的网格和函数空间来创建真实的 fem.Function 和 ufl.Form
                # domain_mock = fem.create_unit_square(self.comm, 2, 2) # MPI.COMM_WORLD
                # V_mock = fem.functionspace(domain_mock, ("CG", 1))
                # self._u = fem.Function(V_mock)
                # v_mock = ufl.TestFunction(V_mock)
                # self._F = ufl.inner(self._u, v_mock) * ufl.dx # 简化形式
                # self._J = ufl.derivative(self._F, self._u) # 简化形式
                pass # 保持简单，依赖于包装器内部的检查
            else: # 模拟对象
                self._u = "mock_u_var"
                self._F = "mock_F_form"
                self._J = "mock_J_form"
            self._bcs = []

        def setup_problem(self): pass
        def get_residual_form(self): return self._F if hasattr(self, '_F') else "mock_F_form"
        def get_jacobian_form(self): return self._J if hasattr(self, '_J') else "mock_J_form"
        def get_boundary_conditions(self): return self._bcs
        def get_variable_to_solve(self): return self._u if hasattr(self, '_u') else "mock_u_var"
        def get_test_function(self): return "mock_test_func"
        def get_trial_function(self): return "mock_trial_func"


    mock_mpi_comm_main = MPI.COMM_WORLD if FENICSX_AVAILABLE else MPI.Comm()
    
    print(f"FEniCSx 可用: {FENICSX_AVAILABLE}")
    
    prob_def_instance = MockProblemDef("test_nl_problem", mock_mpi_comm_main)
    solver_settings_instance = MockSimParams() # 传递包含 nonlinear_solver_config 的对象

    try:
        nl_solver = NonlinearSolverWrapper(prob_def_instance, solver_settings_instance, mock_mpi_comm_main)
        print(f"创建的求解器: {nl_solver}")
        if nl_solver._is_initialized_correctly:
            nl_solver.solve()
            print(f"求解后: {nl_solver}")
        else:
            print("求解器未正确初始化，跳过求解。")
    except Exception as e_main:
        print(f"运行 NonlinearSolverWrapper 示例时发生主错误: {e_main}")
        import traceback
        traceback.print_exc() 